Hu, Xin2017-07-112017-07-1120172017-07-11http://hdl.handle.net/1828/8315The analysis of characteristics of large-scale graphs has shown tremendous bene ts in social networks, spam detection, epidemic disease control, analyzing software systems and so on. However, today, processing graph algorithms on massive datasets is not an easy task not only because of the large data volume, but also the complexity of the graph algorithm. Therefore, a number of large-scale processing platforms have been developed to tackle these problems. GraphChi is a popular system that is capable of executing massive graph datasets on a single PC. Some researchers claim that GraphChi has the same or even better performance, compared with distributed graphanalytics platforms such as the popular Apache Giraph. In this paper, we implement a well-optimized k-core decomposition algorithm on Giraph. Then we provide a comparison of the performance of running the k-core decomposition algorithm in Giraph and GraphChi using various graph datasets.enAvailable to the World Wide Webvertex-centric modelgraph theoryApache GiraphGraphChiproject